Viewing-point detection system using specific image processing for eye-position tracking autostereoscopic display

1999 ◽  
Author(s):  
Hiroshi Imai ◽  
Susumu Tsujikawa ◽  
Masao Imai
Keyword(s):  
Image Processing ◽  
Detection System ◽  
Position Tracking ◽  
Eye Position ◽  
Autostereoscopic Display ◽  
Point Detection

Crosstalk Minimization Method for Eye-tracking-based 3D Display

2020 ◽  
Author(s):  
Seok Lee ◽  
Juyong Park ◽  
Dongkyung Nam
Keyword(s):  
Image Processing ◽  
Eye Tracking ◽  
Three Dimensional ◽  
Processing Method ◽  
Eye Position ◽  
Relative Location ◽  
3D Display ◽  
Minimization Method ◽  
3D Crosstalk ◽  
Pixel Value

In this article, the authors present an image processing method to reduce three-dimensional (3D) crosstalk for eye-tracking-based 3D display. Specifically, they considered 3D pixel crosstalk and offset crosstalk and applied different approaches based on its characteristics. For 3D pixel crosstalk which depends on the viewer’s relative location, they proposed output pixel value weighting scheme based on viewer’s eye position, and for offset crosstalk they subtracted luminance of crosstalk components according to the measured display crosstalk level in advance. By simulations and experiments using the 3D display prototypes, the authors evaluated the effectiveness of proposed method.

Traffic incident detection system based on video image processing

2008 ◽  
Vol 28 (7) ◽  
pp. 1886-1889 ◽  
Author(s):  
Qin WANG ◽  
Shan HUANG ◽  
Hong-bin ZHANG ◽  
Quan YANG ◽  
Jian-jun ZHANG
Keyword(s):  
Image Processing ◽  
Detection System ◽  
Video Image ◽  
Incident Detection ◽  
Traffic Incident ◽  
Video Image Processing

Image detection and parameterization for different components in cross-sections of radial tires

2021 ◽  
pp. 095440702110236
Author(s):  
Chen Liu ◽  
Yude Dong ◽  
Yanli Wei ◽  
Jiangtao Wang ◽  
Hongling Li
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Cross Sections ◽  
Digital Image ◽  
Detection Method ◽  
Detection System ◽  
Practical Significance ◽  
Vehicle Safety ◽  
Wire Steel ◽  
Good Detection

The internal structure analysis of radial tires is of great significance to improve vehicle safety and during tire research. In order to perform the digital analysis and detection of the internal composition in radial tire cross-sections, a detection method based on digital image processing was proposed. The research was carried out as follows: (a) the distribution detection and parametric analysis of the bead wire, steel belt, and carcass in the tire section were performed by means of digital image processing, connected domain extraction, and Hough transform; (b) using the angle of location distribution and area relationship, the detection data were optimized through coordinate and quantity relationship constraints; (c) a detection system for tire cross-section components was designed using the MATLAB platform. Our experimental results showed that this method displayed a good detection performance, and important practical significance for the research and manufacture of tires.

Eye-position detection system

2000 ◽  
Author(s):  
Holger Heidrich ◽  
Armin Schwerdtner ◽  
Andreas Glatte ◽  
Hartmut Mix
Keyword(s):  
Detection System ◽  
Eye Position ◽  
Position Detection

Eye-position tracking stereoscopic display using image-shifting optics

1996 ◽  
Author(s):  
Hiroshi Imai ◽  
Masao Imai ◽  
Yukio Ogura ◽  
Keiichi Kubota
Keyword(s):  
Position Tracking ◽  
Eye Position ◽  
Stereoscopic Display ◽  
Image Shifting

Automatic lung nodule detection system using image processing techniques in computed tomography

2020 ◽  
Vol 56 ◽  
pp. 101659 ◽  
Author(s):  
Chung-Feng Jeffrey Kuo ◽  
Chang-Chiun Huang ◽  
Jing-Jhong Siao ◽  
Chia-Wen Hsieh ◽  
Vu Quang Huy ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Processing ◽  
Detection System ◽  
Lung Nodule ◽  
Image Processing Techniques ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Processing Techniques

scholarly journals Lung Cancer Detection using Convolutional Neural Network

2019 ◽  
Vol 8 (6) ◽  
pp. 3256-3262 ◽  
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Detection System ◽  
Histogram Equalization ◽  
Input Image ◽  
Machine Learning Algorithms ◽  
Morphological Operations ◽  
Image Processing Techniques ◽  
Cad System ◽  
Processing Techniques

The mortality rate is increasing among the growing population and one of the leading causes is lung cancer. Early diagnosis is required to decrease the number of deaths and increase the survival rate of lung cancer patients. With the advancements in the medical field and its technologies CAD system has played a significant role to detect the early symptoms in the patients which cannot be carried out manually without any error in it. CAD is detection system which has combined the machine learning algorithms with image processing using computer vision. In this research a novel approach to CAD system is presented to detect lung cancer using image processing techniques and classifying the detected nodules by CNN approach. The proposed method has taken CT scan image as input image and different image processing techniques such as histogram equalization, segmentation, morphological operations and feature extraction have been performed on it. A CNN based classifier is trained to classify the nodules as cancerous or non-cancerous. The performance of the system is evaluated in the terms of sensitivity, specificity and accuracy

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